Baxter Robot 2D Image Processing

This week, I collaborated with Dounia Bakalem on the development of 2D image processing algorithms for the Baxter robot. Our primary focus was on transforming real-time video feeds into binary reference images for enhanced robot control and perception.

The project aimed to create robust algorithms that enable the Baxter robot to reliably detect and interact with objects in its environment, despite challenges such as lighting variations and camera imperfections.

1. Background Removal and Noise Reduction

We implemented algorithms to remove background clutter and mitigate the impact of lighting variations and camera imperfections. This step aimed to achieve a high degree of image consistency, enabling reliable object detection for robot control.

2. Object Size Estimation

We explored methods for estimating the diameter of a spherical object (ball) within the image frame. This estimation relied on an initial configuration (e.g., known object size in a reference image) and subsequent image processing techniques.

This project provided valuable insights into several key areas:

Image Preprocessing for Robot Perception

• The importance of preprocessing real-world video data to create robust and consistent input for robot control systems
• Techniques for handling lighting variations and camera noise in industrial environments
• Methods for achieving consistent image quality across different operational conditions

Object Feature Extraction

• Techniques for extracting relevant object features (e.g., diameter) from 2D images to guide robot actions
• Development of robust measurement algorithms that work with varying object positions and orientations
• Integration of size estimation with robot manipulation strategies

The developed algorithms serve as a foundation for further exploration of image-based control strategies for Baxter robots. Potential applications include:

• Industrial Automation: Quality control and object sorting in manufacturing environments
• Adaptive Manipulation: Real-time adjustment of robot behavior based on object characteristics
• Enhanced Perception: Integration with other sensing modalities for comprehensive environment understanding
• Collaborative Robotics: Improved human-robot interaction through better visual perception